Stochastic production scheduling model with generalised Pareto rate of production and Weibull decay

2017 ◽  
Vol 1 (3) ◽  
pp. 278
Author(s):  
B. Muniswamy ◽  
Dasari Madhulatha ◽  
K. Srinivasa Rao
Keyword(s):  
Production Scheduling ◽  
Scheduling Model ◽  
Stochastic Production ◽  
Stochastic Production Scheduling

scholarly journals Integrated Parametric Graph Closure and Branch-and-Cut Algorithm for Open Pit Mine Scheduling under Uncertainty

Mining ◽  
2022 ◽  
Vol 2 (1) ◽  
pp. 32-51
Author(s):  
Devendra Joshi ◽  
Amol Paithankar ◽  
Snehamoy Chatterjee ◽  
Sk Md Equeenuddin
Keyword(s):  
Production Scheduling ◽  
Resource Constraints ◽  
Deterministic Model ◽  
Open Pit Mine ◽  
Branch And Cut ◽  
Open Pit ◽  
Computational Time ◽  
Scheduling Problems ◽  
Stochastic Production ◽  
Stochastic Production Scheduling

Open pit mine production scheduling is a computationally expensive large-scale mixed-integer linear programming problem. This research develops a computationally efficient algorithm to solve open pit production scheduling problems under uncertain geological parameters. The proposed solution approach for production scheduling is a two-stage process. The stochastic production scheduling problem is iteratively solved in the first stage after relaxing resource constraints using a parametric graph closure algorithm. Finally, the branch-and-cut algorithm is applied to respect the resource constraints, which might be violated during the first stage of the algorithm. Six small-scale production scheduling problems from iron and copper mines were used to validate the proposed stochastic production scheduling model. The results demonstrated that the proposed method could significantly improve the computational time with a reasonable optimality gap (the maximum gap is 4%). In addition, the proposed stochastic method is tested using industrial-scale copper data and compared with its deterministic model. The results show that the net present value for the stochastic model improved by 6% compared to the deterministic model.

Lube Production Scheduling Model at the AGIP-Petroli’s Livorno Refinery

1999 ◽  
pp. 77-90 ◽  
Author(s):  
Sergio Barbariol ◽  
Mauro Lusetti ◽  
Marco Mantilli ◽  
Mauro Scarioni
Keyword(s):  
Production Scheduling ◽  
Scheduling Model

A new knowledgeable encapsulation method of steel production scheduling model

2020 ◽  
Vol 234 (14) ◽  
pp. 1673-1685
Author(s):  
Xiaowu Chen ◽  
Guozhang Jiang ◽  
Gongfa Li ◽  
Ying Zuo ◽  
Feng Xiang
Keyword(s):  
Production Scheduling ◽  
Steel Production ◽  
Mapping Method ◽  
Interface Model ◽  
The Core ◽  
Scheduling Model ◽  
Encapsulation Method ◽  
The Relationship ◽  
Knowledge Encapsulation ◽  
Web Platform

The steel production scheduling is a typical continuous/discrete hybrid process; it is dynamic and difficult to predict. The scheduling model is the core object of steel production scheduling, and its modeling methods directly affect the precise decision-making and execution efficiency of scheduling. However, the current linear program and simulation model do not yet realize the scheduling model quick reuse and dynamic construction. Therefore, a new model knowledgeable encapsulation method is proposed, which consists of a knowledgeable encapsulation framework and knowledgeable mapping method. The knowledgeable encapsulation framework includes the model knowledge description interface, model knowledge publication interface, model knowledge behavior interface, and a web platform. The interfaces and the platform are designed to help model providers to encapsulate the scheduling model in an open network environment. The mapping method is constructed to strengthen the relationship between the model knowledge. Finally, a knowledge encapsulation platform is established to verify the effectiveness of the model knowledge encapsulation method.

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